Metrics.
We refer to the metrics in CNN-Cert [1]}. Concretely, we evaluate Ti-Lin and other methods on 10 random test images.
As for effectiveness, we use \(\frac{100(\epsilon _{cert}^{\prime }-\epsilon _{cert})}{\epsilon _{cert}}\%\)  to quantify the percentage of improvement, where \(\epsilon _{cert}^{\prime }\)  and \(\epsilon _{cert}\)  represent the robustness bounds certified by Ti-Lin and other comparative tools, respectively. We color result tables' cells in orange from light to dark by checking whether the value of improvement(%) is in \([0,20), [20,40), [40,60),\)  or \( [60,+\infty )\) . As for efficiency, we record the average computation time over the correctly-classified images.
